Ryph vs Exabase
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Ryph compared with Exabase
| Detail | 
Ryph | 
Exabase |
|---|
| What it does | Open-source AI agent orchestration platform for incident response and automation
| Context infrastructure for AI agents
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| Stage | Not given
| Startup
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| Founded | Not given
| Not given
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| Based in | Not given
| GB
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| Pricing model | Open source
| Freemium
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| Pricing | Not given
| Not given
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| Key features | - Incident response with agent investigation and pull request generation
- Automated workflows triggered on schedules or webhooks
- Agent summoning from GitHub, GitLab and Radicle with /ryph command
- Semantic memory system for shared and persistent agent learning
- Credentials kept behind gateways with no access in sandbox
- Customized environments per repository with cached dependencies
- Browser use for agent interactions with live watching
- Artifacts and pages for reports and overviews
- Deployment options from laptop to Kubernetes
- Tool proxy with allowlisted MCP server integrations
| - Memory system with self-managing advanced memory
- Isolated per-tenant instances with version rollback
- Portable context server with files, notes and links
- Sub-document multi-modal hybrid search
- Structured data extraction from PDFs, websites and images
- Autonomous agents for knowledge base automation
- 99.9% uptime SLA
- AES-256 encryption at rest and SSL in transit
- TypeScript SDK and REST API
- Model-agnostic and framework-agnostic
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| What makes it different | - Agents work without holding real credentials or keys
- Changes ship as patches that require review before merge
- Runs on your own infrastructure from laptop to cloud
- Semantic memory learns across runs with deduplication and contradiction handling
- Built-in safety assuming agents can be manipulated or exploited
| - Cut token spend by up to 81% through persistent agent memory instead of full context window stuffing
- State-of-the-art on two leading AI memory benchmarks
- Full-stack data layer eliminating need for multiple services
- Production-tested at scale powering Fabric platform
- Self-improving memory that tracks relationships and resolves contradictions
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| Free plan | Not given
| Not given
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| Open source | Not given
| Not given
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| Platforms | Not given
| Not given
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| Public API | Not given
| Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 29 ● no change since last check |
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| Trust Flow (Majestic) | 0 | 11 |
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| Citation Flow (Majestic) | 0 | 32 |
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| Referring domains (Majestic) | 0 | 122 |
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4 details filled in for both companies.
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